Difference between revisions of "CS4646 Spring 2018"

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==DRAFT==
 
 
This page is still in progress.  Don't consider it final until the first day of class.
 
 
 
==Overview==
 
==Overview==
  

Revision as of 16:04, 10 January 2018

Overview

You are on the page for information specific to the Spring 2018 session of CS 4646. Go here (Undergrad_ML4T) for overall course policies.

Schedule & Forum

  • Project Deadlines: All projects are due Sunday night at 11:55 PM Eastern US Time. Projects are due at the end of the week in which they are listed. For example, Project 1 (assess a portfolio) is listed as due in Week 3, meaning Sunday 1/28 -- the Sunday after Week 3.
  • Late Projects: As stated in class, projects will be accepted up to 24 hours late without any excuse required. Projects one second to 24 hours late will receive a -10 penalty. After 24 hours, late projects will not be accepted for any credit at all unless arrangements were made with the instructor prior to the project deadline.
  • Schedule: Subject to Change if necessary. I will give you as much notice as possible.


Week Date (Tues) Weekly Topics Due
1 1/9 Course Overview, Python/Pandas Tutorial
2 1/16 Numpy Tutorial, Visualizing Market Data, Working with Time Series, Incomplete Data
3 1/23 ML Lexicon/Taxonomy, Evaluating Learners Project 1
4 1/30 Supervised Learning (KNN, LinReg, Decision Trees)
5 2/6 Ensembles (Bagging, Boosting), Market History, Actors Project 2
6 2/13 Order Types, Order Book, Leverage
7 2/20 Valuation, Technical Analysis, Candlestick Chart Patterns Project 3
8 2/27 Time Value of Money, CAPM, Efficient Market Hypothesis
9 3/6 Fund. Law of APM, Efficient Frontier, Review Exam 1
10 3/13 Finite Automata, MDP, Value/Policy Iteration, Drop Day (3/14) Project 4
11 3/20 SPRING BREAK
12 3/27 Q-Learning
13 4/3 Q-Learning, Misc ML Topics or Catch-up Project 5
14 4/10 Options, Time Series Q-Learning
15 4/17 Review, Exam 2, Last regular day of class Exam 2
16 4/24 Final Instruction Days (Tuesday), Help Session on Final Project Project 6
17 5/1 Finals week, NO FINAL EXAM

Assignments

Projects (60%)

  • [assess_portfolio] 5% (easy)
  • Regression / Ensemble Learners 10% (challenging)
  • Market Simulator 10% (moderate)
  • Manual Strategy 10% (moderate)
  • Q-Learning Robot 10% (moderate)
  • Strategy Learner 15% (very challenging)

Exams (40%)

  • Exam 1: 20%
  • Exam 2: 20%

Exam Study Guides

Thresholds

  • A: 90% and above
  • B: 80% and above
  • C: 70% and above
  • D: 60% and above
  • F: below 60%

These are hard boundaries (we round down).